Method for processing a digital input signal of a channel equalizer

ABSTRACT

The invention concerns a method for treating a baseband digital signal at the output of an adapted filter ( 9 ) and at the input of a channel equalizer ( 13 ), which consists in subjecting said signal to a whitening filter ( 10 ) whereof the coefficients for each signal block are estimated taking into account the interfering signals in the frequency band of the useful signal and in one or several adjacent bands. That process consists in projecting an autocorrelation vector calculated from the signal block received from different directions corresponding to predetermined correlation vectors related to the useful signal, to signals possibly present in the adjacent bands and to thermal noise. The whitening filter coefficients are then deduced from the sum of projected vectors, taking into account the useful signal energy if the latter has not been removed before calculating the autocorrelations.

[0001] The present invention relates to the field of radiocommunicationsand in particular to the processing performed in a receiver upstream ofa digital equalizer.

[0002] It applies in systems where the available spectrum is subdividedinto adjacent frequency bands so as to support different communications.

[0003] An example is the so-called GSM (“Global System for Mobilecommunications”) European cellular radiocommunication system in whichthe spectrum allocated around 900 or 1800 MHz is subdivided intofrequency bands spaced 200 kHz apart, each of these bands forming thesubject of a time-division multiplexing according to the TDMA scheme(“Time Division Multiple Access”). The GSM system uses a frequencyhopping technique to combat the channel fading and to increase thecapacity of the system. GSM frequency hopping consists, for acommunication set up on a TDMA channel defined in particular by a giventime slot of successive TDMA frames, in changing the communicationfrequency from one frame to the next from among the carriers, spaced 200kHz apart, allocated to the system, according to a hopping pattern whichis known to the transmitter and to the receiver.

[0004] In a radiocommunication receiver, the filtering operationsperformed upstream of the channel equalizer may correspond to a fixed oradaptive filter.

[0005] The specification of a fixed filter depends on the assumptionsmade about the noise and the interferers. If the filter is optimized toenhance the performance in terms of sensitivity, then the robustness ofthe receiver to interferers which may be present in the adjacentfrequency channels is decreased. Conversely, an excellent filter asregards robustness to interferers in the adjacent channels degrades theperformance in terms of sensitivity.

[0006] An adaptive filter makes it possible to achieve a bettercompromise. However, the use of an adaptive filter has hitherto beenpossible only in the case of stationary channels, making it possible todetermine the reception filter in a reliable manner.

[0007] In certain cases, this stationarity condition is not fulfilled.For example, in the GSM system, the frequency hopping at the TDMA framerate of 4.615 ms, carried out between two signal bursts transmitted intwo successive frames, constantly modifies the interference conditionsand therefore the optimal structure of the reception filter.

[0008] It is conventional to decompose the filtering upstream of thechannel equalizer into the cascading of a filter matched to the spectrumof the modulation and of a filter in the Nyquist band, referred to as“whitening” filter. This whitening filter must guarantee noise residuals(thermal noise+interference of the other channels) which are asindependent as possible at the input of the channel equalizer. It isknown that this structure affords an optimal protection scheme (see G.D. Forney Jr.: “Maximum-Likelihood Sequence Estimation of DigitalSequences in the Presence of Intersymbol Interference”, IEEE Trans.Inform. Theory, Vol. IT-18, May 1972, pages 363-378).

[0009] The present invention aims to allow adaptive estimation of thewhitening filter, even in the presence of a weakly stationarytransmission channel, such as that of GSM.

[0010] The invention thus proposes a method of processing a basebanddigital signal block at an output of a filter matched to a radiotransmission channel and at an input of a channel equalizer, wherein thebaseband signal is fed to a whitening filter estimated for the block bya procedure comprising the steps of:

[0011] calculating an autocorrelation vector of the baseband signal;

[0012] determining a modified autocorrelation vector as a sum ofpredetermined vectors weighted by respective coefficients, thepredetermined vectors comprising a first vector representative of anautocorrelation of a useful signal in a first frequency band, at leastone second vector representative of a correlation of the useful signalwith a disturbance from at least one channel situated in a secondfrequency band adjacent to the first band and a third vectorrepresentative of a correlation of the useful signal with a thermalnoise component, said coefficients being chosen to minimize a deviationbetween the calculated autocorrelation vector and said modifiedautocorrelation vector;

[0013] estimating a noise autocorrelation vector by subtracting thefirst vector multiplied by an energy estimation of the useful signalfrom the modified autocorrelation vector; and

[0014] estimating coefficients of the whitening filter from theestimated noise autocorrelation vector.

[0015] Alternatively, the procedure for estimating the whitening filtercomprises the steps of:

[0016] estimating a noise sequence included in the baseband signal blockand superimposed on a known signal sequence;

[0017] calculating an autocorrelation vector of the estimated noisesequence;

[0018] estimating a noise autocorrelation vector in the form of a sum ofpredetermined vectors weighted by respective coefficients, thepredetermined vectors comprising a first vector representative of anautocorrelation of a useful signal in a first frequency band, at leastone second vector representative of a correlation of the useful signalwith a disturbance from at least one channel situated in a secondfrequency band adjacent to the first band and a third vectorrepresentative of a correlation of the useful signal with a thermalnoise component, said coefficients being chosen to minimize a deviationbetween the calculated autocorrelation vector and said estimated noiseautocorrelation vector; and

[0019] estimating coefficients of the whitening filter from theestimated noise autocorrelation vector.

[0020] The estimation of the whitening filter takes account of thestructure of the disturbing signals in the frequency band of the usefulsignal (co-channel interference due to the reuse of the frequencies, andbroadband thermal noise) and in one or more adjacent frequency bands(interference from adjacent channels, and broadband thermal noise).

[0021] The calculated autocorrelation vector (of the baseband signal, oralternatively of the estimated noise sequence) is projected onto variousdirections which correspond to predetermined correlation vectorsrelating to the useful signal (and to the co-channel interferers) and tothe signals liable to be present in the adjacent channels. Thisprojection amounts to a least squares optimization which eliminates theautocorrelation vector estimation errors, and thus allows reliableestimation of the whitening filter, even if just one block of the signalis available to perform this estimation.

[0022] The size of the block is chosen in such a way that theinterference conditions are substantially stationary over the durationof a block. In the case of a GSM type signal, a block will typicallycorrespond to a signal burst transmitted in a time slot of the TDMAframe. Between two successive bursts, the communication frequency canchange, thereby modifying the conditions of co-channel interference andinterference in the adjacent channels.

[0023] Another aspect of the present invention pertains to a device forprocessing a baseband digital signal in a radiocommunication receiver,comprising a whitening filter for receiving the baseband signal at anoutput of a filter matched to a transmission channel and for supplying afiltered signal to a channel equalizer, and means of estimation of thewhitening filter for a block of the baseband signal.

[0024] For the implementation of the first method above, the means ofestimation of the whitening filter comprise:

[0025] means for calculating an autocorrelation vector of the basebandsignal;

[0026] means for determining a modified autocorrelation vector as a sumof predetermined vectors weighted by respective coefficients, thepredetermined vectors comprising a first vector representative of anautocorrelation of a useful signal in a first frequency band, at leastone second vector representative of a correlation of the useful signalwith a disturbance from at least one channel situated in a secondfrequency band adjacent to the first band and a third vectorrepresentative of a correlation of the useful signal with a thermalnoise component, said coefficients being chosen to minimize a deviationbetween the calculated autocorrelation vector and said modifiedautocorrelation vector;

[0027] means of estimation of a noise autocorrelation vector bydifferencing between the modified autocorrelation vector and the firstvector multiplied by an energy estimation of the useful signal; and

[0028] means of estimation of the coefficients of the whitening filterfrom the estimated noise autocorrelation vector.

[0029] For the implementation of the aforesaid alternative method, themeans of estimation of the whitening filter comprise:

[0030] means of estimation of a noise sequence included in the basebandsignal block and superimposed on a known signal sequence;

[0031] means for calculating an autocorrelation vector of the estimatednoise sequence;

[0032] means of estimation of a noise autocorrelation vector in the formof a sum of predetermined vectors weighted by respective coefficients,the predetermined vectors comprising a first vector representative of anautocorrelation of a useful signal in a first frequency band, at leastone second vector representative of a correlation of the useful signalwith a disturbance from at least one channel situated in a secondfrequency band adjacent to the first band and a third vectorrepresentative of a correlation of the useful signal with a thermalnoise component, said coefficients being chosen to minimize a deviationbetween the calculated autocorrelation vector and said estimated noiseautocorrelation vector; and

[0033] means of estimation of coefficients of the whitening filter fromthe estimated noise autocorrelation vector.

[0034] Other features and advantages of the present invention willbecome apparent in the description hereinbelow of non-limiting exemplaryembodiments, with reference to the appended drawings, in which:

[0035]FIG. 1 is a schematic diagram of a radiocommunication receiverimplementing the present invention; and

[0036]FIGS. 2 and 3 are flowcharts of whitening filter estimationprocedures which can be used in the receiver of FIG. 1.

[0037] The receiver represented in FIG. 1 comprises an antenna 1 forpicking up radio signals. In the remainder of the present description,these radio signals will be regarded as of GSM type, without this beinglimiting.

[0038] GSM uses a plurality of carrier frequencies having spaced by 200kHz. The spectral shaping of the signals is designed to minimize theinterference between the adjacent frequency channels. However, aninterference residual persists which adds to the noise picked up in theband of the useful signal. Moreover, a residual of co-channelinterference due to other communications on the same carrier frequencyin a distant cell also persists on account of the reuse of thefrequencies in geographically separated cells. These disturbances getadded to the broadband thermal noise.

[0039] The modulation used to send the GSM signal can be of GMSK type(“Gaussian Minimum Shift Keying”) for traditional GSM channels, or ofEDGE type (“Enhanced Data for GSM Evolution”).

[0040] The radio signal picked up by the antenna 1 is amplified by anamplifier 2, then subjected to a bandpass filtering by a radiofrequencyfilter 3. This signal is transposed to an intermediate frequency by amixer 4 which mixes it with a wave delivered by a local oscillator 5. Abandpass filter 6 retains only the useful frequency component at theoutput of the mixer 4, and the resulting intermediate frequency signalis digitized by an analog/digital converter 7.

[0041] The intermediate frequency digital signal is converted intobaseband, by taking account of the frequency hopping pattern relating tothe relevant communication, then fed to a matched filtering. In FIG. 1,the baseband conversion and matched filtering operations are showndiagrammatically by two distinct modules 8, 9. In practice, the samemodule can effect both operations.

[0042] The matched filter 9 has a response matched to that of thevarious filters used at the transmitter of the signal, as well as those3, 6 of the receiver. If c(t) denotes the waveform of the shaping pulse,integrating the filters of the receiver, the matched filter 9essentially performs a convolution of the baseband signal with theresponse c*(τ−t), where τ is a propagation delay. Of course, the pulsec(t) differs depending on whether the signal sent is of GMSK or EDGEtype.

[0043] The output signal from the matched filter 9 is sampled at thefrequency of the symbols sent. The samples of a signal blockcorresponding to a TDMA burst are denoted S_(k), for 0≦k<L. The block iscomposed of L=148 symbols, the L′=26 central symbols being symbols knowna priori forming a learning sequence.

[0044] The baseband signal S_(k) delivered by the matched filter 9 isfed on the one hand to a whitening filter 10 and on the other hand to apropagation channel probing module 11.

[0045] The channel probing module 11 estimates in a known manner theimpulse response of the transmission channel over a length of L″+1samples (the memory of the channel being for example L″=4), and, afterconvolution with that of the whitening filter, supplies it to thechannel equalizer 13 which processes the signal delivered by thewhitening filter 10. The channel equalizer 13 operates for exampleaccording to the Viterbi algorithm (see G. D. Forney Jr.: “The ViterbiAlgorithm”, Proc. of the IEEE, Vol. 61, No. 3, March 1973, pages268-278). Its output signal is supplied to the decoders situateddownstream of the receiver for utilization.

[0046] The role of the whitening filter 10 is to impart a uniformspectrum to the residual noise of the signal emanating from the matchedfilter 9, thereby affording the best performance of the channelequalizer 13.

[0047] In a known manner, if K(z) denotes the z transform of theautocorrelation vector of the noise and if K(z) is factorized in theform K(z)=R(z)·R(z⁻¹)*, then the z transform of the optimal whiteningfilter is given by F(z)=1/R(z).

[0048] If the whitening filter 10 is constructed with a finite impulseresponse of length p (for example p=4), then this response$F = \begin{pmatrix}f_{0} \\f_{1} \\f_{2} \\\vdots \\f_{p - 1}\end{pmatrix}$

[0049] is obtained by inverting the noise autocorrelation matrix H. Itis for example the first column of the matrix H⁻¹:

F=H ⁻¹ ·e ₁  (1)

[0050] with $e_{1} = {{\begin{pmatrix}1 \\0 \\0 \\\vdots \\0\end{pmatrix}\quad {and}\quad H} = {\begin{pmatrix}K_{0} & K_{1}^{*} & K_{2}^{*} & \cdots & K_{p - 1}^{*} \\K_{1} & K_{0} & K_{1}^{*} & ⋰ & \vdots \\K_{2} & K_{1} & K_{0} & ⋰ & K_{2}^{*} \\\vdots & ⋰ & ⋰ & ⋰ & K_{1}^{*} \\K_{p - 1} & \cdots & K_{2} & K_{1} & K_{0}\end{pmatrix}.}}$

[0051] The autocorrelation vector $K = \begin{pmatrix}K_{0} \\K_{1} \\K_{2} \\\vdots \\K_{p - 1}\end{pmatrix}$

[0052] determines the first column of the matrix H which is Hermitianand of Toeplitz structure.

[0053] The problem of the estimation of the optimal whitening filter can10 therefore be reduced to the problem of the correct estimation of thenoise autocorrelation vector K over a single signal burst.

[0054] This problem is dealt with by the estimation module 14 of thereceiver which solves it by using a priori information about theinterferers (co-channel and in the adjacent channels).

[0055] The module 14 seeks to model the spectrum of the colored noisepresent at the output of the matched filter 9 as being the mixture ofQ+Q′+2 distinct spectra corresponding respectively:

[0056] to the co-channel interference (band q=0);

[0057] to the thermal noise;

[0058] to the interference emanating from channels corresponding to Qadjacent frequency bands below that of the relevant channel (bands q<0);

[0059] to the interference emanating from channels corresponding to Q′adjacent frequency bands above that of the relevant channel (bands q>0).

[0060] In a typical embodiment, we shall take Q=Q′=1. It would bepossible to take Q=0 (respectively Q′=0) in the case of a channelsituated at the bottom end (respectively top end) of the GSM spectrum,but this is not compulsory.

[0061] M_(q) denotes the column vector of size p whose components arethe normalized correlations of orders 0 to p−1 of the signal of band 0with the signal originating from band q after the matched filtering(−Q≦q≦Q′). Furthermore, M_(N) denotes the column vector of size p whosecomponents are the normalized correlations of orders 0 to p−1 of thesignal of band 0 with the thermal noise after the matched filtering, andM denotes the p×(Q+Q′+2) matrix given by M=(M_(−Q), M_(−Q+1), . . . ,M_(Q′), M_(N)).

[0062] All the components of the vectors M_(q) and M_(N), and hence ofthe matrix M are constants known a priori. They depend simply on thespectrum of the modulation and the filtering elements of thetransmission chain. One possibility is to calculate them from pulseshapes measured over a specimen of the receiver at the output of thematched filter 9. These constants are determined once and for all(calculated and/or measured) and stored by the estimation module 14.

[0063] The module 14 performs an estimation of the autocorrelationvector of the signal received in the form of a linear combination of theQ+Q′+2 vectors M_(q) (−Q≦q≦Q′) and M_(N). This estimation consists of aprojection onto the space spanned by these Q+Q′+2 vectors, thisamounting to minimizing the autocorrelation vector estimation noise.

[0064] The autocorrelation vector X such as observed by the receiver isdecomposed into the form: $\begin{matrix}{X = {{\sum\limits_{q = {- Q}}^{Q^{\prime}}\quad {a_{q} \cdot M_{q}}} + {N_{0} \cdot M_{N}} + W}} & (2)\end{matrix}$

[0065] and the estimation reduces to that of the coefficients a_(−Q),a_(−Q+1), . . . , a_(Q), et N₀.

[0066] Denoting the vector composed of these Q+Q′+2 estimations by$\hat{A} = \begin{pmatrix}{\hat{a}}_{- Q} \\\vdots \\{\hat{a}}_{0} \\\vdots \\{\hat{a}}_{Q^{\prime}} \\{\hat{N}}_{0}\end{pmatrix}$

[0067] the minimizing of the energy of the estimation noise W consistsin taking:

Â=Re(M ^(H) M)⁻¹ Re(M ^(H) X)  (3)

[0068] where Re(.)denotes the real part and (.)^(H) the conjugatetranspose. The estimated autocorrelation vector is then {circumflex over(X)}=M.Â, i.e. a sum of the predetermined vectors M_(−Q), M_(−Q+1), . .. , M_(Q′), M_(N) respectively weighted by the coefficients â_(−Q),â_(−Q+1), . . . , â_(Q), and {circumflex over (N)}₀.

[0069] In the embodiment illustrated by FIG. 2, the autocorrelationvector X on which the estimation module 14 operates is composed ofautocorrelations of the samples S_(k) of the output signal from thematched filter 9, these autocorrelations being calculated in step 20over the length L of the signal burst: $\begin{matrix}{X_{i} = {\frac{1}{L - i}{\sum\limits_{k = 0}^{L - i - 1}\quad {S_{k}^{*} \cdot S_{k + i}}}}} & (4)\end{matrix}$

[0070] In step 21, the vector of coefficients Â is estimated accordingto relation (3), the matrices M^(H) and Re(M^(H)M)⁻¹ having beencalculated once and for all and stored in the module 14. In step 22, theestimated autocorrelation vector {circumflex over (X)} is obtained byforming the product of the matrix M times the vector Â calculatedpreviously.

[0071] Among the operations performed by the channel probing module 11,there is the estimation of the per-symbol energy of the useful signalcontained in the received signal. This energy â_(u) is typicallyestimated by correlation on the basis of a known learning sequenceinserted into the signal block sent. The energy estimation â_(u) issupplied to the module 14 which obtains the estimation of theautocorrelation vector {circumflex over (K)} of the noise by subtractingthe vector â_(u).M₀ from {circumflex over (X)} in step 23.

[0072] The module 14 then constructs the Toeplitz Hermitian matrix Ĥfrom the estimated autocorrelation vector {circumflex over(K)}={circumflex over (X)}−â_(u).M₀ ({circumflex over (K)} is the firstcolumn of Ĥ), then it proceeds to the inversion of the matrix Ĥ in step24. To carry out this inversion, various conventional algorithms forinverting Toeplitz matrices may be used, such as for example theLevinson-Durbin algorithm. In step 25, the module 14 obtains theestimation {circumflex over (F)} of the whitening filter as in relation(1): {circumflex over (F)}=Ĥ⁻¹.e₁.

[0073] The components {circumflex over (f)}₀, {circumflex over (f)}₁, .. . , {circumflex over (f)}_(p−1) of this vector {circumflex over (F)}are supplied to the whitening filter 10 so that it applies them to thefinite impulse response filtering of the current signal block.

[0074] In the alternative embodiment represented in FIG. 3, theautocorrelations X_(i) are calculated directly on the basis of anestimated noise sequence obtained by the channel probing module 11.These estimations of the noise N_(k) are obtained by the module 11 onlyfor the samples k corresponding to the learning sequence, withoutinfluence of the unknown information symbols, i.e. for(L−L′)/2+L″≦k<(L+L′)/2. They are obtained by subtracting the learningsequence convolved with the estimated impulse response of the channelfrom the corresponding samples S_(k).

[0075] The calculation of the autocorrelations X_(i) is performed instep 30 according to: $\begin{matrix}{X_{i} = {\frac{1}{L^{\prime} - L^{''} - i}{\sum\limits_{k = {{(\frac{L - L^{\prime}}{2})} + L^{''}}}^{{(\frac{L + L^{\prime}}{2})} - i - 1}\quad {N_{k}^{*} \cdot N_{k + i}}}}} & (5)\end{matrix}$

[0076] In step 31, the module 14 calculates the vector Â according torelation (3), then the estimation {circumflex over (K)} of theautocorrelation vector of the noise is obtained directly in step 32 bythe product M.Â, the useful component having already been removed.

[0077] The procedure according to FIG. 3 terminates with the invertingof the matrix Ĥ (step 33) and with the obtaining of the components ofthe vector {circumflex over (F)} in step 34. These steps 33 and 34 areexecuted in the same way as steps 24 and 25 in the embodiment accordingto FIG. 2.

[0078] It has been observed that the above method, allowing optimizationof the whitening filter upstream of a Viterbi equalizer, afforded anappreciable improvement in the robustness of the receiver tointerference. In the example of GSM in an urban environment with GMSKmodulation, the improvement in the channel-to-interferers ratio (C/I)for a binary error rate (BER) of 1% may reach several decibels for theco-channel interferers and of the order of some ten decibels for theinterferers in the adjacent channels.

1. A method of processing a baseband digital signal block at an outputof a filter matched to a radio transmission channel and at an input of achannel equalizer, wherein the baseband signal is fed to a whiteningfilter estimated for the block by a procedure comprising the steps of:calculating an autocorrelation vector of the baseband signal;determining a modified autocorrelation vector as a sum of predeterminedvectors weighted by respective coefficients, the predetermined vectorscomprising a first vector representative of an autocorrelation of auseful signal in a first frequency band, at least one second vectorrepresentative of a correlation of the useful signal with a disturbancefrom at least one channel situated in a second frequency band adjacentto the first band and a third vector representative of a correlation ofthe useful signal with a thermal noise component, said coefficientsbeing chosen to minimize a deviation between the calculatedautocorrelation vector and said modified autocorrelation vector;estimating a noise autocorrelation vector by subtracting the firstvector multiplied by an energy estimation of the useful signal from themodified autocorrelation vector; and estimating coefficients of thewhitening filter from the estimated noise autocorrelation vector.
 2. Amethod of processing a baseband digital signal block at an output of afilter matched to a radio transmission channel and at an input of achannel equalizer, wherein the baseband signal is fed to a whiteningfilter estimated for the block by a procedure comprising the steps of:estimating a noise sequence included in the baseband signal block andsuperimposed on a known signal sequence; calculating an autocorrelationvector of the estimated noise sequence; estimating a noiseautocorrelation vector in the form of a sum of predetermined vectorsweighted by respective coefficients, the predetermined vectorscomprising a first vector representative of an autocorrelation of auseful signal in a first frequency band, at least one second vectorrepresentative of a correlation of the useful signal with a disturbancefrom at least one channel situated in a second frequency band adjacentto the first band and a third vector representative of a correlation ofthe useful signal with a thermal noise component, said coefficientsbeing chosen to minimize a deviation between the calculatedautocorrelation vector and said estimated noise autocorrelation vector;and estimating coefficients of the whitening filter from the estimatednoise autocorrelation vector.
 3. The method as claimed in claim 1,wherein the estimation of the coefficients of the whitening filtercomprises the steps of: forming a Toeplitz Hermitian matrix having afirst column defined by the estimated noise autocorrelation vector;inverting said matrix; extracting the estimated coefficients of thewhitening filter from the first column of the inverted matrix.
 4. Themethod as claimed in claim 1, wherein each baseband digital signal blockcorresponds to a GSM signal burst.
 5. A device for processing a basebanddigital signal in a radiocommunication receiver, comprising a whiteningfilter for receiving the baseband signal at an output of a filtermatched to a transmission channel and for supplying a filtered signal toa channel equalizer, and means of estimation of the whitening filter fora block of the baseband signal, wherein the means of estimation of thewhitening filter comprise: means for calculating an autocorrelationvector of the baseband signal; means for determining a modifiedautocorrelation vector as a sum of predetermined vectors weighted byrespective coefficients, the predetermined vectors comprising a firstvector representative of an autocorrelation of a useful signal in afirst frequency band, at least one second vector representative of acorrelation of the useful signal with a disturbance from at least onechannel situated in a second frequency band adjacent to the first bandand a third vector representative of a correlation of the useful signalwith a thermal noise component, said coefficients being chosen tominimize a deviation between the calculated autocorrelation vector andsaid modified autocorrelation vector; means of estimation of a noiseautocorrelation vector by differencing between the modifiedautocorrelation vector and the first vector multiplied by an energyestimation of the useful signal; and means of estimation of thecoefficients of the whitening filter from the estimated noiseautocorrelation vector.
 6. A device for processing a baseband digitalsignal in a radiocommunication receiver, comprising a whitening filterfor receiving the baseband signal at an output of a filter matched to atransmission channel and for supplying a filtered signal to a channelequalizer, and means of estimation of the whitening filter for a blockof the baseband signal, wherein the means of estimation of the whiteningfilter comprise: means of estimation of a noise sequence included in thebaseband signal block and superimposed on a known signal sequence; meansfor calculating an autocorrelation vector of the estimated noisesequence; means of estimation of a noise autocorrelation vector in theform of a sum of predetermined vectors weighted by respectivecoefficients, the predetermined vectors comprising a first vectorrepresentative of an autocorrelation of a useful signal in a firstfrequency band, at least one second vector representative of acorrelation of the useful signal with a disturbance from at least onechannel situated in a second frequency band adjacent to the first bandand a third vector representative of a correlation of the useful signalwith a thermal noise component, said coefficients being chosen tominimize a deviation between the calculated autocorrelation vector andsaid estimated noise autocorrelation vector; and means of estimation ofcoefficients of the whitening filter from the estimated noiseautocorrelation vector. 7 The device as claimed in claim 5, wherein themeans of estimation of the coefficients of the whitening filter comprisemeans for forming a Toeplitz Hermitian matrix having a first columndefined by the estimated noise autocorrelation vector, means forinverting said matrix and means for extracting the estimatedcoefficients of the whitening filter from the first column of theinverted matrix.
 8. The device as claimed in claim 5, wherein eachbaseband digital signal block corresponds to a GSM signal burst.
 9. Themethod as claimed in claim 2, wherein the estimation of the coefficientsof the whitening filter comprises the steps of: forming a ToeplitzHermitian matrix having a first column defined by the estimated noiseautocorrelation vector; inverting said matrix; extracting the estimatedcoefficients of the whitening filter from the first column of theinverted matrix.
 10. The method as claimed in claim 2, wherein eachbaseband digital signal block corresponds to a GSM signal burst.
 11. Thedevice as claimed in claim 6, wherein the means of estimation of thecoefficients of the whitening filter comprise means for forming aToeplitz Hermitian matrix having a first column defined by the estimatednoise autocorrelation vector, means for inverting said matrix and meansfor extracting the estimated coefficients of the whitening filter fromthe first column of the inverted matrix.
 12. The device as claimed inclaim 6, wherein each baseband digital signal block corresponds to a GSMsignal burst.